A synthesis of dryland restoration techniques.

Purpose

To quantitatively examine the efficacy of vegetation restoration in drylands globally.

Questions

1. What is the global extent of research that directly examined restoration of drylands?
   
2. What were the common measures?
   
3. Is the restoration of vegetation a common and primary focus?
   
4. How frequently does the restoration measure outcomes beyond the focal species?
   
5. What were the primary restoration goals as reported by primary authors?
   
6. How much variation was there in the techniques tested and how long were experiments monitored and tested?
   
7. How relatively effective were the techniques?

Step 1. Search

Study-level viz to document patterns in exclusions primarily and the relatie frequenices, at the study level, of major categories of evidence.

#study data####
library(tidyverse)
studies <- read_csv("data/studies.csv")
studies

## # A tibble: 278 x 18
##       ID title technique data  region exclude rationale observations
##    <dbl> <chr> <chr>     <chr> <chr>  <chr>   <chr>     <chr>       
##  1   152 Shor… seeding,… expe… Africa no      <NA>      <NA>        
##  2   180 Rest… chemical… App.… Africa no      <NA>      <NA>        
##  3   229 Infl… soil see… fiel… Africa no      <NA>      <NA>        
##  4   230 Acti… planting  fiel… Africa no      <NA>      <NA>        
##  5   255 The … grazing … fiel… Africa no      <NA>      <NA>        
##  6   262 Reve… seeding,… eper… Africa no      <NA>      <NA>        
##  7   263 The … phytogen… fiel… Africa no      <NA>      <NA>        
##  8   264 Eval… seeding,… fiel… Africa no      <NA>      <NA>        
##  9   271 Patc… natural … fiel… Africa no      <NA>      <NA>        
## 10     4 Fact… natural … App.… Africa no      <NA>      <NA>        
## # ... with 268 more rows, and 10 more variables: disturbance <chr>,
## #   system <chr>, goal <chr>, intervention <chr>, paradigm <chr>,
## #   grazing <chr>, hypothesis <chr>, soil <chr>, benchmark <chr>,
## #   notes <chr>

#quick look at rationale needed
exclusions <- studies %>%
  filter(exclude == "yes")

#quick look at studies with paradigms
evidence <- studies %>%
  filter(exclude == "no")

#library(skimr)
#skim(evidence)

#study-level viz#####
#exclusions
ggplot(exclusions, aes(rationale, fill = region)) +
  geom_bar() +
  coord_flip() +
  labs(x = "rational for exclusion", y = "frequency") +
  scale_fill_brewer(palette = "Paired")

ggplot(evidence, aes(disturbance, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

ggplot(evidence, aes(region, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

ggplot(evidence, aes(data, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

ggplot(evidence, aes(system, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

ggplot(evidence, aes(goal, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(x = "outcome", y = "frequency")

#step 1 models####
#paradigm
derived.evidence <- evidence %>%
  group_by(technique, data, region, disturbance, goal, paradigm) %>% summarise(n = n())

#active-passive split
m <- glm(n~paradigm, family = poisson, derived.evidence)
anova(m, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##          Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                       167     9.9147         
## paradigm  1 0.045115       166     9.8696   0.8318

#region
m1 <- glm(n~paradigm*region, family = poisson, derived.evidence)
#m1
#summary(m1)
anova(m1, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##                 Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                              167     9.9147         
## paradigm         1 0.045115       166     9.8696   0.8318
## region           6 0.301367       160     9.5682   0.9995
## paradigm:region  6 0.213627       154     9.3546   0.9998

#outcome
m2 <- glm(n~paradigm*goal, family = poisson, derived.evidence)
#m1
#summary(m1)
anova(m2, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##               Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                            167     9.9147         
## paradigm       1 0.045115       166     9.8696   0.8318
## goal           6 0.240941       160     9.6287   0.9997
## paradigm:goal  4 0.301480       156     9.3272   0.9897

#even split between active and passive evidence by all key categories

Step 2. Sort

A summary of sort process using PRISMA.

library(PRISMAstatement)
prisma(found = 1504,
       found_other = 5,
       no_dupes = 1039, 
       screened = 1039, 
       screen_exclusions = 861, 
       full_text = 178,
       full_text_exclusions = 100, 
       qualitative = 100, 
       quantitative = 78,
       width = 800, height = 800)

Step 3. Synthesize

Check data and calculate necessary measures.

data <- read_csv("data/data.csv")
data <- data %>%
  mutate(lrr = log(mean.t/mean.c), rii = ((mean.t-mean.c)/(mean.t + mean.c)), var.es = ((sd.t^2/n.t*mean.t^2) + (sd.c^2/n.c*mean.c^2)))
data

## # A tibble: 3,291 x 48
##    study.ID    ID publication.year data.year exp.year `exp.length (mo…
##       <dbl> <dbl>            <dbl>     <dbl>    <dbl>            <dbl>
##  1        1   1.1             2018        NA       NA               NA
##  2        1   1.1             2018        NA       NA               NA
##  3        1   1.1             2018        NA       NA               NA
##  4        1   1.2             2018        NA       NA               NA
##  5        1   1.2             2018        NA       NA               NA
##  6        1   1.2             2018        NA       NA               NA
##  7        1   1.3             2018        NA       NA               NA
##  8        1   1.3             2018        NA       NA               NA
##  9        1   1.3             2018        NA       NA               NA
## 10        1   1.4             2018        NA       NA               NA
## # ... with 3,281 more rows, and 42 more variables: disturbance <chr>,
## #   focus <chr>, technique <chr>, paradigm <chr>, hypothesis <chr>,
## #   pathway <chr>, plant.species <chr>, target.plant <chr>,
## #   measure.success <chr>, measured.factor <chr>, factor.levels <chr>,
## #   treatment <chr>, control <chr>, unit <chr>, Nsites <dbl>, n.t <dbl>,
## #   n.c <dbl>, ntotalsamples <dbl>, mean.t <dbl>, mean.c <dbl>,
## #   sd.t <dbl>, sd.c <dbl>, se.t <dbl>, se.c <dbl>, `p-value` <dbl>,
## #   df <dbl>, measure.dispersion <chr>, lat <dbl>, long <dbl>,
## #   continent <chr>, country <chr>, ecosystem <chr>, `elevation
## #   (m)` <dbl>, `MAP (mm)` <dbl>, aridity.index <dbl>,
## #   `potential.evaporation (mm)` <dbl>, grazing <chr>, soil <chr>,
## #   notes <chr>, lrr <dbl>, rii <dbl>, var.es <dbl>

#consider adding some other effect size measures and/or study-level data too

Step 4. Summarize

Explore summary level data of all data. Explore aggregation levels that support the most reasonable data structure and minimize non-independence issues.

#evidence map####
require(maps)
world<-map_data("world")
map<-ggplot() + geom_polygon(data=world, fill="gray50", aes(x=long, y=lat, group=group))
map + geom_point(data=data, aes(x=long, y=lat)) #render a literal map, i.e. evidence map, of where we study the niche in deserts globally

#aggregation####
data.simple <- data %>%
  group_by(study.ID, paradigm, technique, measure.success) %>%
  summarise(n = n(), mean.lrr = mean(lrr), mean.rii = mean(rii), mean.var = mean(var.es))

ggplot(na.omit(data.simple), aes(technique, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired")

ggplot(na.omit(data.simple), aes(measure.success, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired")

Step 5. Statistics

Meta and conventional statistical models to explore relative efficacy.

library(plotrix) #for quick s.e. calculations sometimes needed for data tidy step
library(meta) #nice package for most meta-statistics

#assign model (typically a nice meta. function from one of several packages such as meta, metafor, or netmeta)
m <- metagen(mean.rii, mean.var, studlab = study.ID, data = data.simple) #fit generic meta-analysis to an object
forest(m)